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Structural biology and drug design as simple as this … de Ruyck Jérôme 30/11/2015 Lille - France Introduction https://en.wikibooks.org/wiki/Structural_Biochemistry Introduction Pre-clinical studies Chemistry Pharmacology 2-4 years Scalingup 2 months - 1 year Clinical trials Phase 1 Phase 2 3-5 years Phase 3 NDA Phase 4 2-3 years Introduction Pre-clinical studies Scientific challenge Chemistry Pharmacology Clinical trials Phase 1 Scalingup Phase 2 Phase 3 Phase 4 150 M $ Money 2-4 years 2 months - 1 year 3-5 years • Challenging • Time consuming • Expensive NDA 2-3 years • Efficiency increased Multidisciplinary approaches • Time saved • Cost effectiveness Introduction In-vitro drug-design In-silico drug-design Medicinal chemistry High-throughput screening Structural biology Bioinformatics Virtual screening Molecular modelling Introduction In-vitro drug design In-silico drug design Medicinal chemistry High-throughput screening Structural biology Bioinformatics Virtual screening Molecular modelling Introduction In-vitro drug design In-silico drug design Medicinal chemistry High-throughput screening Structural biology Bioinformatics Virtual screening Molecular modelling Molecular biology / Bioinformatics Target Structural biology / Molecular modelling HTS Hits Medicinal chemistry Leads Drugs Pharmacology / PK-PD predictions Introduction In-silico drug design Ligand-based drug design Target Structure-based drug design Hits Leads Drugs Outline Virtual screening Ligand-based drug design Pharmacophore Structure-based drug design Ligand Docking Optimization Protein Lead design In situ design Fragment Pharmacophore Evaluation Nomenclature Different kind of inter- and intramolecular interactions Polar interactions Dipoles-Dipoles Hydrophobic interactions VdW – H-Bond Aliphatic Electrostatic Aromatic Salt bridges Nomenclature Aromatic interactions Quadropular interactions π – π interactions π – cation interactions Nomenclature Quantum Mechanics (QM) Computational Chemistry Theoretical Chemistry Molecular Mechanics (MM) Molecular Modelling Molecular Dynamics Deriving information on molecular systems without really synthesizing them ! Quantum mechanics • Nuclei and electrons separated • Time consuming • Applied to small molecules • Not suitable for proteins Method Accuracy Max atoms Semiempirical Low 2000 HF & DFT Medium 500 Perturbation methods High 50 Coupled-cluster Very High 20 Molecular mechanics 𝐸 𝑡𝑜𝑡 = 𝑏𝑜𝑛𝑑𝑠 𝐾𝑟 𝑟 − 𝑟0 2 + Streching 𝑎𝑛𝑔𝑙𝑒𝑠 𝐾Θ Θ − Θ0 + Bending • Spheres and springs model • Very quick • Can be applied to small molecules • Suitable for proteins • Accuracy depends on a force field 𝑉𝑛 𝑑𝑖ℎ𝑒𝑑𝑟𝑎𝑙𝑠 2 1 + 𝑐𝑜𝑠 𝑛τ − 𝛾 Torsion + 𝐴𝑖𝑗 𝑖<𝑗 12 𝑟𝑖𝑗 − 𝐵𝑖𝑗 6 𝑟𝑖𝑗 + Non-bonded 𝑞𝑖 𝑞𝑗 𝜀𝑟𝑖𝑗 Force field 𝐸 𝑡𝑜𝑡 = 𝑏𝑜𝑛𝑑𝑠 𝐾𝑟 𝑟 − 𝑟0 Streching 2 + 𝑎𝑛𝑔𝑙𝑒𝑠 𝐾Θ Θ − Θ0 + 𝑑𝑖ℎ𝑒𝑑𝑟𝑎𝑙𝑠 2 Bending • Different force fields for different systems • • • • Proteins Sugars Metals … • Different parameterization • Empirical • Semi-empirical (including QM) 𝑉𝑛 1 + 𝑐𝑜𝑠 𝑛τ − 𝛾 Torsion + 𝐴𝑖𝑗 𝑖<𝑗 12 𝑟𝑖𝑗 − 𝐵𝑖𝑗 6 𝑟𝑖𝑗 + Non-bonded 𝑞𝑖 𝑞𝑗 𝜀𝑟𝑖𝑗 Direct vs Indirect approaches Virtual screening Pharmacophore Ligand IC50 / Ki 2D Structures Docking Ligand-based drug design (Indirect approach) Protein 3D structures Lead design In situ design Fragment 2D Structures Pharmacophore Structure-based drug design (Direct approach) Direct vs Indirect approaches Indirect approach Direct approach Don’t know receptor Know Ligands Know receptor Don’t know ligand Direct vs Indirect approaches Indirect approach Direct approach Don’t know receptor Know Ligands Know receptor Don’t know ligand ? Statistical methods Structural Biology Pharmacophore 3D-QSAR Protein - ligand interactions Docking Ligand-based drug design Virtual screening Ligand-based drug design Pharmacophore Ligand IC50 / Ki 2D Structures Optimization Lead design Fragment 2D Structures Pharmacophore Selection Evaluation Pharmacophore A pharmacophore is a geometrical description of molecular features which are necessary for molecular recognition of a ligand by a biological macromolecule. Typical pharmacophore features include hydrophobic centroids, aromatic rings, hydrogen bond acceptors or donors, cations, and anions. Pharmacophore Inhibition data generation Structural superimposition Example (1) “A four-point pharmacophore of COX-2 selective inhibitors was derived from a training set of 16 compounds, using the Catalyst program. It consists of a H bond acceptor, two hydrophobic groups and an aromatic ring, in accordance with SAR data of the compounds and with topology of the COX-2 active site. This hypothesis, combined with exclusion volume spheres representing important residues of the COX-2 binding site, was used to virtually screen the Maybridge database. Eight compounds were selected for an in vitro enzymatic assay. Five of them show COX-2 inhibition close to that of nimesulide and rofecoxib, two reference COX-2 selective inhibitors. As a result, structurebased pharmacophore generation was able to identify original lead compounds, inhibiting the COX-2 isoform.” Example (2) “Apolar trisubstituted derivatives of harmine show high antiproliferative activity on diverse cancer cell lines. However, these molecules present a poor solubility making these compounds poorly bioavailable. Here, new compounds were synthesized in order to improve solubility while retaining antiproliferative activity. First, polar substituents have shown a higher solubility but a loss of antiproliferative activity. Second, a Comparative Molecular Field Analysis (CoMFA) model was developed, guiding the design and synthesis of eight new compounds. Characterization has underlined the in vitro antiproliferative character of these compounds on five cancerous cell lines, combining with a high solubility at physiological pH, making these molecules druggable. Moreover, targeting glioma treatment, human intestinal absorption and blood brain penetration have been calculated, showing high absorption and penetration properties.” 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 908 717 526 408 343 290 236 11211 10817 10263 9757 9220 8697 67843 60473 53286 46615 40410 76020 84763 93645 101667 NMR 8176 7609 6956 34256 X-Ray 196 155 136 5991 28733 24295 19898 16306 13776 11389 5130 4254 110 87 75 3525 2994 46 2553 22 2139 13 Structural biology improvement PDB STATISTICS CryoEM 2015 Structure-based drug design Virtual screening Structure-based drug design Ligand IC50 / Ki 2D Structures Docking Optimization Protein 3D structures Lead design In situ design Fragment 2D Structures Selection Evaluation Structure-based drug design Virtual screening Structure-based drug design Ligand IC50 / Ki 2D Structures Docking Optimization Protein 3D structures Lead design In situ design Fragment 2D Structures Selection Evaluation Molecular docking Protein-ligand docking is a computational method that mimics the binding of a ligand to a protein to form a complex. It predicts the pose of the molecule in the binding site and calculates a score representing the strength of the binding. Protein Ligand Docking Binding site Complex How does it work ? Protein-ligand docking software works in two different steps Search algorithm Scoring function Generates a large number of poses of a molecule in the active site Calculates a score or binding affinity for a particular pose Genetic Lamarckian Simulated annealing Forcefield-based Empirical Knowledge-based potentials Performs automated docking with full acyclic ligand flexibility, partial cyclic ligand flexibility and partial protein flexibility in and around active site. Scoring: includes H-bonding term, pairwise dispersion potential (hydrophobic interactions), molecular and mechanics term for internal energy. Example (1) “Crystal structures of Thermus thermophilus and Bacillus subtilis type 2 IPP isomerases were combined to generate an almost complete model of the FMN-bound structure of the enzyme. In contrast to previous studies, positions of flexible loops were obtained and carefully analyzed by molecular dynamics. Docking simulations find a unique putative binding site for the IPP substrate.” Example (2) “A total of 1,990 compounds from the National Cancer Institute (NCI) diversity set with nonredundant structures have been tested to inhibit cancer cell lines with unknown mechanism. Cancer inhibition through EGFR-TK is one of the mechanisms of these compounds. In this work, we performed receptor-based virtual screening against the NCI diversity database. Using two different docking algorithms, AutoDock and Gold, combined with subsequent post-docking analyses, we found eight candidate compounds with high scoring functions that all bind to the ATP-competitive site of the kinase. None of these compounds belongs to the main group of the currently known EGFR-TK inhibitors. Binding mode analyses revealed that the way these compounds complexed with EGFR-TK differs from quinazoline inhibitor binding and the interaction mainly involves hydrophobic interactions. Our results suggest that these molecules could be developed as novel lead compounds in anti-cancer drug design.” Structure-based drug design Virtual screening Structure-based drug design Ligand IC50 / Ki 2D Structures Docking Optimization Protein 3D structures Lead design In situ design Fragment 2D Structures Selection Evaluation Fragment-based drug design Fragment-based drug design is the screening of libraries of fragments with low chemical complexity. The fragments usually bind the protein target with low affinity (high mM). The fragments selected for follow-up are then optimized by addition of chemical moieties or linked together with the aim of obtaining a highly potent drug or inhibitor. Examples (1) “The search for new drugs is plagued by high attrition rates at all stages in research and development. Chemists have an opportunity to tackle this problem because attrition can be traced back, in part, to the quality of the chemical leads. Fragment-based drug discovery (FBDD) is a new approach, increasingly used in the pharmaceutical industry, for reducing attrition and providing leads for previously intractable biological targets. FBDD identifies low-molecular-weight ligands (~150 Da) that bind to biologically important macromolecules. The threedimensional experimental binding mode of these fragments is determined using X-ray crystallography or NMR spectroscopy, and is used to facilitate their optimization into potent molecules with drug like properties. Compared with high-throughput-screening, the fragment approach requires fewer compounds to be screened, and, despite the lower initial potency of the screening hits, offers more efficient and fruitful optimization campaigns.” Examples (2) “X-ray crystallography is an established technique for ligand screening in fragment-based drug-design projects, but the required manual handling steps – soaking crystals with ligand and the subsequent harvesting – are tedious and limit the throughput of the process. Here, an alternative approach is reported: crystallization plates are pre-coated with potential binders prior to protein crystallization and X-ray diffraction is performed directly ‘in situ’ (or in-plate). Its performance is demonstrated on distinct and relevant therapeutic targets currently being studied for ligand screening by X-ray crystallography using either a bending-magnet beamline or a rotating-anode generator. The possibility of using DMSO stock solutions of the ligands to be coated opens up a route to screening most chemical libraries.” The future is now … Quantum Mechanics (QM) Computational Chemistry Theoretical Chemistry Molecular Mechanics (MM) Molecular Modeling Molecular Dynamics Deriving information on molecular systems without really synthesizing them ! Hybrid QM/MM Simulations Computational Biology The ONIOM method 𝐸 𝑂𝑁𝐼𝑂𝑀, 𝑅𝑒𝑎𝑙 = 𝐸 𝑙𝑜𝑤, 𝑟𝑒𝑎𝑙 − 𝐸 𝑙𝑜𝑤, 𝑚𝑜𝑑𝑒𝑙 + 𝐸(ℎ𝑖𝑔ℎ, 𝑚𝑜𝑑𝑒𝑙) S. Dapprich, et al. 1999 THEOCHEM. 461-462: 1 Inside the mechanism “Here, we report an integrated quantum mechanics/molecular mechanics (QM/MM) study of the bioorganometallic reaction pathway of the reduction of (E)-4-hydroxy-3-methylbut-2-enyl pyrophosphate (HMBPP) into the so called universal terpenoid precursors isopentenyl pyrophosphate (IPP) and dimethylallyl pyrophosphate (DMAPP), promoted by the IspH enzyme. Dehydroxylation of HMBPP is triggered by a proton transfer from Glu126 to the OH group of HMBPP. The reaction pathway is completed by competitive proton transfer from the terminal phosphate group to the C2 or C4 atom of HMBPP.” Mechanism-based drug design “Development of novel influenza neuraminidase inhibitors is critical for preparedness against influenza outbreaks. Knowledge of the neuraminidase enzymatic mechanism and transition state analogue, 2-deoxy-2,3-didehydro-N-acetylneuraminic acid, contributed to the development of the first generation anti-neuraminidase drugs, zanamivir and oseltamivir. However, lack of evidence regarding influenza neuraminidase key catalytic residues has limited strategies for novel neuraminidase inhibitor design. Here, we confirm that influenza neuraminidase conserved Tyr406 is the key catalytic residue that may function as a nucleophile; thus, mechanism-based covalent inhibition of influenza neuraminidase was conceived. Crystallographic studies reveal that 2a,3ax-difluoro-N-acetylneuraminic acid forms a covalent bond with influenza neuraminidase Tyr406 and the compound was found to possess potent anti-influenza activity against both influenza A and B viruses. Our results address many unanswered questions about the influenza neuraminidase catalytic mechanism and demonstrate that covalent inhibition of influenza neuraminidase is a promising and novel strategy for the development of next-generation influenza drugs.” Acknowledgment • Computational Molecular Systems Biology team Dr. M. Lensink Dr. R. Blossey Dr. J. Bouckaert Dr. T. Dumych Dr. E.-M. Krammer Ir. G. Brysbaert • Fundings